Compared with SOTA.
We present the adaptation results on tasks GTA5 \(\rightarrow \)  Cityscapes and SYNTHIA \(\rightarrow \)  Cityscapes in Table REF  and Table REF  respectively, with comparisons to the state-of-the-art feature-space DA methods [1]}, [2]}, [3]}, [4]}. We also present the current state-of-the-art pixel-space and segmentation-space DA in the tables. In Table REF , not surprisingly, SIBAN significantly outperforms the source-only segmentation method by \(+16.3\%\)  on VGG-16 and \(+6.0\%\)  on ResNet-101 since the source-only segmentation method does not consider the domain shift. Besides, SIBAN outperforms the state-of-the-art feature-space methods, which improves the mIOU by over \(+2.5\%\)  compared with FCNs [1]}, AdaptSeg [3]}, and CyCADA [2]}. Compared to the DA methods in the segmentation and pixel space [3]}, [2]}, SIBAN can also be on par with them. In some infrequent classes which are prone to suffer from the side effect of information bottleneck, e.g., fence, traffic light, and rider, we can observe that SIBAN can significantly outperform IBAN. The results verify the effectiveness of SIB module to protect the uncommon classes from being eliminated. Similar results can be observed in Table REF . Some qualitative segmentation examples can be viewed in Fig. REF .
